C4.5: programs for machine learning
C4.5: programs for machine learning
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Lullaby: a capture & access system for understanding the sleep environment
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Sinabro: opportunistic and unobtrusive mobile electrocardiogram monitoring system
Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
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How we feel is greatly influenced by how well we sleep. Emerging quantified-self apps and wearable devices allow people to measure and keep track of sleep duration, patterns and quality. However, these approaches are intrusive, placing a burden on the users to modify their daily sleep related habits in order to gain sleep data; for example, users have to wear cumbersome devices (e.g., a headband) or inform the app when they go to sleep and wake up. In this paper, we present a radically different approach for measuring sleep duration based on a novel best effort sleep (BES) model. BES infers sleep using smartphones in a completely unobtrusive way -- that is, the user is completely removed from the monitoring process and does not interact with the phone beyond normal user behavior. A sensor-based inference algorithm predicts sleep duration by exploiting a collection of soft hints that tie sleep duration to various smartphone usage patterns (e.g., the time and length of smartphone usage or recharge events) and environmental observations (e.g., prolonged silence and darkness). We perform quantitative and qualitative comparisons between two smartphone only approaches that we developed (i.e., BES model and a sleep-with-the-phone approach) and two popular commercial wearable systems (i.e., the Zeo headband and Jawbone wristband). Results from our one-week 8-person study look very promising and show that the BES model can accurately infer sleep duration (± 42 minutes) using a completely "hands off" approach that can cope with the natural variation in users' sleep routines and environments.